Different Smoothing Window Lengths can Estimate the Neuromuscular Fatigue Threshold at The same Intensity of the Lactate Threshold During the Leg Press Exercise
Bibliographic record
Abstract
Background: This study aimed to compare different smoothing window lengths used to compute the RMS and their impact on RMS slope during leg press exercise and to compare the RMS slope behavior with lactate threshold. Methods: Twelve subjects performed an incremental test on a leg press machine where blood lactate concentration was measured at each stage. The RMS of vastus medialis (VM), vastus lateralis (VL) and rectus femuralis (RF) were computed for 200, 1500 and 3000ms windows length periods, and the RMS linear slope was used to interpret the results in the amplitude domain. The EMG fatigue threshold (EMGth) was determined for the quadriceps muscle during the three smoothing window lengths. Results: There was no significant difference ( p > 0.05) in RMS slope between the three different RMS window length analyses for VM and VL muscles. The RMS slope was significantly higher ( p ≤ 0.05) for the window length period of 1500 and 3000ms compared to 200ms in some intensities of exercise. The ICCs between the RMS slopes were 0.94 for RF and 0.95 for VL and VM. There was no significant difference ( p > 0.05) between the EMGth at different window length periods and the lactate threshold (28.0 ± 3.7% of 1RM). Conclusion: Different smoothing window lengths to computed RMS could be used during resistance exercise without differences in RMS slope. The smoothing window lengths don´t influence EMGth intensity and are related to the lactate threshold.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".